Required Components for RAG with Azure OpenAI

Quick Answer

A vector database like Azure AI Search is one piece of a RAG pipeline that only works because of what feeds into it: an embedding model, such as text-embedding-ada-002, converts both the incoming user query and the chunks of source documents into dense numerical vectors that represent their meaning, not just their literal words. Those vectors are what the vector database actually stores and searches over — without an embedding step first, there's nothing meaningful for the database to hold, since raw text alone doesn't support similarity search based on semantic meaning. The database's job is to take the query's vector and rapidly find the document vectors most similar to it, which is the retrieval half of Retrieval-Augmented Generation; the retrieved chunks are then injected into the prompt sent to the generation model, which is the third piece completing the pipeline. Each component has a distinct job: the embedding model translates text into a comparable form, the vector database finds the closest matches, and the generation model produces the final answer grounded in what was retrieved. Recognizing that these are three necessary, non-overlapping stages — not interchangeable options — is what a 'which components are required' RAG question is testing.